ABSTRACT

The need for intelligent and adaptive cyber security solutions is critical due to the ever evolving and complex nature of cyber threats. This monograph reveals the possibilities offered by generative AI models in cybersecurity, particularly in the areas of threat simulation and anomaly detection. In detail, it provides an overview of the present threat landscape and describes how generative models like GANs, VAEs, and Transformers can be used to perform sophisticated emulation, training data synthesis, real-time anomalous behavior detection, and attack detection. The work discusses also explores the design systems, methodologies, and ethics of generative AI model training that define its trustworthiness and governance. With the aid of interdisciplinary case studies and synthesis approaches, the monograph underscores the advanced potentials along with emerging vulnerabilities of employing generative AI in cyber defense operations. I hope this work becomes a starting point for researchers, practitioners, and strategists seeking to understand and aid in intelligent cyber defense leveraging AI.

Keywords: Generative AI, Cybersecurity, Threat Simulation, Anomaly Detection, GANs, VAEs, Transformers, Synthetic Data, Adversarial Attacks, Cyber Threat Intelligence, Behavioral Analysis, AI Ethics, Real-Time Monitoring, Deep Learning, Cyber Defense

How to Cite this Monograph:

Mathivilasini, S., Sridevi, D., & Anandapriya, B. (2025). Generative AI for Cybersecurity: Threat Simulation and Anomaly Detection. Jupiter Publications Consortium. ISBN: 978-93-86388-79-7. DOI: https://www.doi.org/10.47715/978-93-86388-79-7